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Abstract
Ushbu maqolada data science sohasi uchun zarur bo‘lgan statistik kompetensiyalar chuqur tahlil qilinadi. Statistik tafakkur, ehtimollik nazariyasi, gipoteza sinovi, regressiya modellari va A/B testlar kabi metodologik yondashuvlarning data science doirasidagi o‘rni yoritilgan. Sohaning rivoji uchun takliflar berilgan va statistik kompetensiyalarning tahliliy qaror qabul qilishdagi ahamiyati asoslab berilgan.
References
1. T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. – New York: Springer, 2009. – 745 p.
2. G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning. – New York: Springer, 2013. – 426 p.
3. D.J. Hand. Statistics: A Very Short Introduction. – Oxford: Oxford University Press, 2008. – 144 p.
4. C. O’Neil. Weapons of Math Destruction. – New York: Crown Publishing, 2016. – 259 p.
5. D.T. Larose. Discovering Knowledge in Data. – New York: Wiley, 2014. – 336 p.
6. F. Provost, T. Fawcett. Data Science for Business. – Sebastopol: O’Reilly Media, 2013. – 414 p.
7. D.S. Moore, W.I. Notz, M.A. Fligner. The Basic Practice of Statistics. – New York: W.H. Freeman, 2017. – 720 p.
8. D.C. Montgomery, G.C. Runger. Applied Statistics and Probability for Engineers. – Hoboken: Wiley, 2018. – 768 p.
9. H. Wickham, G. Grolemund. R for Data Science. – Sebastopol: O’Reilly Media, 2017. – 522 p.
10. A. Gelman, J. Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models. – Cambridge: Cambridge University Press, 2007. – 625 p.
11. P. Bruce, A. Bruce. Practical Statistics for Data Scientists. – Sebastopol: O’Reilly Media, 2017. – 318 p.
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